Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations178396
Missing cells0
Missing cells (%)0.0%
Duplicate rows5550
Duplicate rows (%)3.1%
Total size in memory23.3 MiB
Average record size in memory137.0 B

Variable types

Categorical4
Numeric12
Boolean1

Alerts

Dataset has 5550 (3.1%) duplicate rowsDuplicates
weather_main is highly imbalanced (51.4%)Imbalance
weather_description is highly imbalanced (52.6%)Imbalance
pressure is highly skewed (γ1 = 159.8659178)Skewed
rain_3h is highly skewed (γ1 = 184.1125344)Skewed
snow_3h is highly skewed (γ1 = 68.8350361)Skewed
wind_speed has 18490 (10.4%) zerosZeros
wind_deg has 24920 (14.0%) zerosZeros
rain_1h has 159008 (89.1%) zerosZeros
rain_3h has 176541 (99.0%) zerosZeros
snow_3h has 178129 (99.9%) zerosZeros
clouds_all has 82194 (46.1%) zerosZeros

Reproduction

Analysis started2024-08-22 04:22:07.695283
Analysis finished2024-08-22 04:23:00.165250
Duration52.47 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

city_name
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Madrid
36267 
Bilbao
35951 
Seville
35557 
Barcelona
35476 
Valencia
35145 

Length

Max length10
Median length8
Mean length7.3887699
Min length6

Characters and Unicode

Total characters1318127
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValencia
2nd rowValencia
3rd rowValencia
4th rowValencia
5th rowValencia

Common Values

ValueCountFrequency (%)
Madrid 36267
20.3%
Bilbao 35951
20.2%
Seville 35557
19.9%
Barcelona 35476
19.9%
Valencia 35145
19.7%

Length

2024-08-21T21:23:00.474053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:23:00.833402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
madrid 36267
20.3%
bilbao 35951
20.2%
seville 35557
19.9%
barcelona 35476
19.9%
valencia 35145
19.7%

Most occurring characters

ValueCountFrequency (%)
a 213460
16.2%
l 177686
13.5%
i 142920
10.8%
e 141735
10.8%
d 72534
 
5.5%
r 71743
 
5.4%
B 71427
 
5.4%
o 71427
 
5.4%
c 70621
 
5.4%
n 70621
 
5.4%
Other values (6) 213953
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1318127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 213460
16.2%
l 177686
13.5%
i 142920
10.8%
e 141735
10.8%
d 72534
 
5.5%
r 71743
 
5.4%
B 71427
 
5.4%
o 71427
 
5.4%
c 70621
 
5.4%
n 70621
 
5.4%
Other values (6) 213953
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1318127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 213460
16.2%
l 177686
13.5%
i 142920
10.8%
e 141735
10.8%
d 72534
 
5.5%
r 71743
 
5.4%
B 71427
 
5.4%
o 71427
 
5.4%
c 70621
 
5.4%
n 70621
 
5.4%
Other values (6) 213953
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1318127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 213460
16.2%
l 177686
13.5%
i 142920
10.8%
e 141735
10.8%
d 72534
 
5.5%
r 71743
 
5.4%
B 71427
 
5.4%
o 71427
 
5.4%
c 70621
 
5.4%
n 70621
 
5.4%
Other values (6) 213953
16.2%

temp
Real number (ℝ)

Distinct20743
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.61861
Minimum262.24
Maximum315.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:01.213844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum262.24
5-th percentile277.25491
Q1283.67
median289.15
Q3295.15
95-th percentile303.53
Maximum315.6
Range53.36
Interquartile range (IQR)11.48

Descriptive statistics

Standard deviation8.0261993
Coefficient of variation (CV)0.027712996
Kurtosis-0.37694406
Mean289.61861
Median Absolute Deviation (MAD)5.77
Skewness0.22500413
Sum51666801
Variance64.419875
MonotonicityNot monotonic
2024-08-21T21:23:01.620064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286.15 2105
 
1.2%
287.15 2061
 
1.2%
289.15 2041
 
1.1%
288.15 1981
 
1.1%
285.15 1870
 
1.0%
290.15 1808
 
1.0%
284.15 1730
 
1.0%
291.15 1643
 
0.9%
283.15 1642
 
0.9%
292.15 1639
 
0.9%
Other values (20733) 159876
89.6%
ValueCountFrequency (%)
262.24 1
 
< 0.1%
264.132 1
 
< 0.1%
264.428 2
< 0.1%
265.091 2
< 0.1%
265.261 3
< 0.1%
265.442 3
< 0.1%
265.6245 1
 
< 0.1%
265.638 1
 
< 0.1%
265.902 3
< 0.1%
266.0235 1
 
< 0.1%
ValueCountFrequency (%)
315.6 1
 
< 0.1%
315.54 1
 
< 0.1%
315.15 4
< 0.1%
315.03 1
 
< 0.1%
314.76 6
< 0.1%
314.7 1
 
< 0.1%
314.63 1
 
< 0.1%
314.54 3
< 0.1%
314.51 1
 
< 0.1%
314.33 2
 
< 0.1%

temp_min
Real number (ℝ)

Distinct18553
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.33044
Minimum262.24
Maximum315.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:01.901299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum262.24
5-th percentile276.15
Q1282.4836
median288.15
Q3293.73013
95-th percentile302.15
Maximum315.15
Range52.91
Interquartile range (IQR)11.246523

Descriptive statistics

Standard deviation7.9554909
Coefficient of variation (CV)0.027591575
Kurtosis-0.32857452
Mean288.33044
Median Absolute Deviation (MAD)5.633
Skewness0.21063421
Sum51436997
Variance63.289836
MonotonicityNot monotonic
2024-08-21T21:23:02.292692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288.15 6028
 
3.4%
286.15 5706
 
3.2%
287.15 5628
 
3.2%
289.15 5582
 
3.1%
285.15 5417
 
3.0%
283.15 5304
 
3.0%
284.15 5238
 
2.9%
290.15 4963
 
2.8%
281.15 4923
 
2.8%
282.15 4816
 
2.7%
Other values (18543) 124791
70.0%
ValueCountFrequency (%)
262.24 1
 
< 0.1%
264.132 1
 
< 0.1%
264.15 4
< 0.1%
264.428 2
 
< 0.1%
265.091 2
 
< 0.1%
265.15 7
< 0.1%
265.261 3
< 0.1%
265.442 3
< 0.1%
265.6245 1
 
< 0.1%
265.638 1
 
< 0.1%
ValueCountFrequency (%)
315.15 6
 
< 0.1%
314.15 19
 
< 0.1%
313.15 39
< 0.1%
312.381 2
 
< 0.1%
312.224 1
 
< 0.1%
312.1675 1
 
< 0.1%
312.15 50
< 0.1%
312.111 1
 
< 0.1%
312.04 1
 
< 0.1%
311.8123438 1
 
< 0.1%

temp_max
Real number (ℝ)

Distinct18591
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.09127
Minimum262.24
Maximum321.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:02.605170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum262.24
5-th percentile278.15
Q1284.65
median290.15
Q3297.15
95-th percentile306.15
Maximum321.15
Range58.91
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.612454
Coefficient of variation (CV)0.029586783
Kurtosis-0.32564377
Mean291.09127
Median Absolute Deviation (MAD)6
Skewness0.30169026
Sum51929518
Variance74.174364
MonotonicityNot monotonic
2024-08-21T21:23:02.964520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288.15 5574
 
3.1%
289.15 5501
 
3.1%
287.15 5354
 
3.0%
286.15 5338
 
3.0%
290.15 5324
 
3.0%
285.15 5211
 
2.9%
284.15 5084
 
2.8%
291.15 5075
 
2.8%
293.15 5051
 
2.8%
292.15 4919
 
2.8%
Other values (18581) 125965
70.6%
ValueCountFrequency (%)
262.24 1
 
< 0.1%
264.132 1
 
< 0.1%
264.428 2
< 0.1%
265.091 2
< 0.1%
265.261 3
< 0.1%
265.442 3
< 0.1%
265.6245 1
 
< 0.1%
265.638 1
 
< 0.1%
265.902 3
< 0.1%
266.0235 1
 
< 0.1%
ValueCountFrequency (%)
321.15 3
 
< 0.1%
320.15 12
 
< 0.1%
319.15 34
 
< 0.1%
318.71 1
 
< 0.1%
318.15 73
< 0.1%
317.59 2
 
< 0.1%
317.15 96
0.1%
317.04 1
 
< 0.1%
316.48 7
 
< 0.1%
316.15 145
0.1%

pressure
Real number (ℝ)

SKEWED 

Distinct190
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1069.2607
Minimum0
Maximum1008371
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:03.370525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile998
Q11013
median1018
Q31022
95-th percentile1030
Maximum1008371
Range1008371
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5969.6319
Coefficient of variation (CV)5.5829525
Kurtosis26697.242
Mean1069.2607
Median Absolute Deviation (MAD)4
Skewness159.86592
Sum1.9075184 × 108
Variance35636505
MonotonicityNot monotonic
2024-08-21T21:23:03.745499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017 11414
 
6.4%
1016 11357
 
6.4%
1018 11321
 
6.3%
1019 10500
 
5.9%
1015 10468
 
5.9%
1020 9371
 
5.3%
1014 9129
 
5.1%
1021 8623
 
4.8%
1013 7687
 
4.3%
1022 7409
 
4.2%
Other values (180) 81117
45.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
594 1
 
< 0.1%
918 1
 
< 0.1%
923 1
 
< 0.1%
927 1
 
< 0.1%
928 13
< 0.1%
929 4
 
< 0.1%
930 6
< 0.1%
931 8
< 0.1%
932 7
< 0.1%
ValueCountFrequency (%)
1008371 1
< 0.1%
1002881 1
< 0.1%
1002241 1
< 0.1%
1001781 1
< 0.1%
1001501 1
< 0.1%
1000951 1
< 0.1%
102153 1
< 0.1%
102132 1
< 0.1%
102075 1
< 0.1%
101983 1
< 0.1%

humidity
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.423457
Minimum0
Maximum100
Zeros63
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:04.042353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q153
median72
Q387
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.902888
Coefficient of variation (CV)0.32010788
Kurtosis-0.61729798
Mean68.423457
Median Absolute Deviation (MAD)16
Skewness-0.52543765
Sum12206471
Variance479.73648
MonotonicityNot monotonic
2024-08-21T21:23:04.432703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 15920
 
8.9%
100 10128
 
5.7%
87 9781
 
5.5%
82 5869
 
3.3%
81 4994
 
2.8%
88 4731
 
2.7%
77 4364
 
2.4%
76 4135
 
2.3%
72 3439
 
1.9%
78 3234
 
1.8%
Other values (90) 111801
62.7%
ValueCountFrequency (%)
0 63
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 4
 
< 0.1%
7 10
 
< 0.1%
8 22
 
< 0.1%
9 48
< 0.1%
10 60
< 0.1%
ValueCountFrequency (%)
100 10128
5.7%
99 413
 
0.2%
98 547
 
0.3%
97 696
 
0.4%
96 1010
 
0.6%
95 831
 
0.5%
94 1669
 
0.9%
93 15920
8.9%
92 915
 
0.5%
91 752
 
0.4%

wind_speed
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4705599
Minimum0
Maximum133
Zeros18490
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:04.792052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum133
Range133
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0959099
Coefficient of variation (CV)0.84835422
Kurtosis97.124156
Mean2.4705599
Median Absolute Deviation (MAD)1
Skewness3.1704034
Sum440738
Variance4.3928383
MonotonicityNot monotonic
2024-08-21T21:23:05.159609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 55201
30.9%
2 34548
19.4%
3 25036
14.0%
0 18490
 
10.4%
4 18313
 
10.3%
5 11683
 
6.5%
6 6794
 
3.8%
7 3779
 
2.1%
8 2127
 
1.2%
9 1154
 
0.6%
Other values (26) 1271
 
0.7%
ValueCountFrequency (%)
0 18490
 
10.4%
1 55201
30.9%
2 34548
19.4%
3 25036
14.0%
4 18313
 
10.3%
5 11683
 
6.5%
6 6794
 
3.8%
7 3779
 
2.1%
8 2127
 
1.2%
9 1154
 
0.6%
ValueCountFrequency (%)
133 1
 
< 0.1%
64 1
 
< 0.1%
54 1
 
< 0.1%
43 1
 
< 0.1%
40 1
 
< 0.1%
38 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
30 3
< 0.1%
29 1
 
< 0.1%

wind_deg
Real number (ℝ)

ZEROS 

Distinct361
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.59119
Minimum0
Maximum360
Zeros24920
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:05.492436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155
median177
Q3270
95-th percentile340
Maximum360
Range360
Interquartile range (IQR)215

Descriptive statistics

Standard deviation116.61193
Coefficient of variation (CV)0.69998855
Kurtosis-1.3660842
Mean166.59119
Median Absolute Deviation (MAD)107
Skewness-0.031376115
Sum29719202
Variance13598.341
MonotonicityNot monotonic
2024-08-21T21:23:05.867410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24920
 
14.0%
220 5111
 
2.9%
320 4419
 
2.5%
330 4314
 
2.4%
230 4131
 
2.3%
240 3650
 
2.0%
290 3616
 
2.0%
280 3599
 
2.0%
270 3573
 
2.0%
310 3573
 
2.0%
Other values (351) 117490
65.9%
ValueCountFrequency (%)
0 24920
14.0%
1 82
 
< 0.1%
2 94
 
0.1%
3 85
 
< 0.1%
4 138
 
0.1%
5 94
 
0.1%
6 85
 
< 0.1%
7 115
 
0.1%
8 87
 
< 0.1%
9 97
 
0.1%
ValueCountFrequency (%)
360 2631
1.5%
359 77
 
< 0.1%
358 80
 
< 0.1%
357 77
 
< 0.1%
356 80
 
< 0.1%
355 103
 
0.1%
354 74
 
< 0.1%
353 117
 
0.1%
352 98
 
0.1%
351 82
 
< 0.1%

rain_1h
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075491827
Minimum0
Maximum12
Zeros159008
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:06.180585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39884735
Coefficient of variation (CV)5.2833182
Kurtosis399.47518
Mean0.075491827
Median Absolute Deviation (MAD)0
Skewness15.893249
Sum13467.44
Variance0.15907921
MonotonicityNot monotonic
2024-08-21T21:23:06.495952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 159008
89.1%
0.3 12795
 
7.2%
0.9 5196
 
2.9%
3 1310
 
0.7%
12 85
 
< 0.1%
2.29 1
 
< 0.1%
0.25 1
 
< 0.1%
ValueCountFrequency (%)
0 159008
89.1%
0.25 1
 
< 0.1%
0.3 12795
 
7.2%
0.9 5196
 
2.9%
2.29 1
 
< 0.1%
3 1310
 
0.7%
12 85
 
< 0.1%
ValueCountFrequency (%)
12 85
 
< 0.1%
3 1310
 
0.7%
2.29 1
 
< 0.1%
0.9 5196
 
2.9%
0.3 12795
 
7.2%
0.25 1
 
< 0.1%
0 159008
89.1%

rain_3h
Real number (ℝ)

SKEWED  ZEROS 

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00037976468
Minimum0
Maximum2.315
Zeros176541
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:06.792810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.315
Range2.315
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0072884454
Coefficient of variation (CV)19.192004
Kurtosis57067.406
Mean0.00037976468
Median Absolute Deviation (MAD)0
Skewness184.11253
Sum67.7485
Variance5.3121437 × 10-5
MonotonicityNot monotonic
2024-08-21T21:23:07.136531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 176541
99.0%
0.01 214
 
0.1%
0.005 187
 
0.1%
0.015 128
 
0.1%
0.025 74
 
< 0.1%
0.012 66
 
< 0.1%
0.1 64
 
< 0.1%
0.002 54
 
< 0.1%
0.09 53
 
< 0.1%
0.03 53
 
< 0.1%
Other values (79) 962
 
0.5%
ValueCountFrequency (%)
0 176541
99.0%
0.001 22
 
< 0.1%
0.002 54
 
< 0.1%
0.0025 29
 
< 0.1%
0.003 7
 
< 0.1%
0.004 13
 
< 0.1%
0.005 187
 
0.1%
0.006 10
 
< 0.1%
0.007 41
 
< 0.1%
0.0075 6
 
< 0.1%
ValueCountFrequency (%)
2.315 1
 
< 0.1%
0.1 64
< 0.1%
0.098 2
 
< 0.1%
0.097 2
 
< 0.1%
0.095 48
< 0.1%
0.093 43
< 0.1%
0.09 53
< 0.1%
0.089 2
 
< 0.1%
0.0875 3
 
< 0.1%
0.087 6
 
< 0.1%

snow_3h
Real number (ℝ)

SKEWED  ZEROS 

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0047629151
Minimum0
Maximum21.5
Zeros178129
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:07.546718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21.5
Range21.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22260384
Coefficient of variation (CV)46.736891
Kurtosis5470.8889
Mean0.0047629151
Median Absolute Deviation (MAD)0
Skewness68.835036
Sum849.685
Variance0.04955247
MonotonicityNot monotonic
2024-08-21T21:23:07.924345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178129
99.9%
1.5 15
 
< 0.1%
2.7 15
 
< 0.1%
0.7 12
 
< 0.1%
0.2 12
 
< 0.1%
0.05 9
 
< 0.1%
0.6 9
 
< 0.1%
0.1 9
 
< 0.1%
1.125 6
 
< 0.1%
2.899 6
 
< 0.1%
Other values (56) 174
 
0.1%
ValueCountFrequency (%)
0 178129
99.9%
0.001 2
 
< 0.1%
0.01 3
 
< 0.1%
0.025 3
 
< 0.1%
0.028 3
 
< 0.1%
0.05 9
 
< 0.1%
0.093 3
 
< 0.1%
0.1 9
 
< 0.1%
0.125 3
 
< 0.1%
0.129 4
 
< 0.1%
ValueCountFrequency (%)
21.5 3
< 0.1%
20.9 3
< 0.1%
19.9 3
< 0.1%
17 3
< 0.1%
15.875 3
< 0.1%
13.2 3
< 0.1%
12.5 3
< 0.1%
8.9 3
< 0.1%
8.5 3
< 0.1%
8.4 3
< 0.1%

clouds_all
Real number (ℝ)

ZEROS 

Distinct97
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.073292
Minimum0
Maximum100
Zeros82194
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:08.336752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q340
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation30.774129
Coefficient of variation (CV)1.2273669
Kurtosis-0.57904886
Mean25.073292
Median Absolute Deviation (MAD)20
Skewness0.94684437
Sum4472975
Variance947.04703
MonotonicityNot monotonic
2024-08-21T21:23:08.649226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82194
46.1%
20 31002
 
17.4%
75 21940
 
12.3%
40 13668
 
7.7%
92 4380
 
2.5%
90 3760
 
2.1%
8 2067
 
1.2%
12 1589
 
0.9%
88 1266
 
0.7%
32 1186
 
0.7%
Other values (87) 15344
 
8.6%
ValueCountFrequency (%)
0 82194
46.1%
2 138
 
0.1%
3 7
 
< 0.1%
4 357
 
0.2%
5 133
 
0.1%
6 260
 
0.1%
7 3
 
< 0.1%
8 2067
 
1.2%
9 18
 
< 0.1%
10 789
 
0.4%
ValueCountFrequency (%)
100 218
 
0.1%
97 2
 
< 0.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
94 2
 
< 0.1%
93 1
 
< 0.1%
92 4380
2.5%
91 27
 
< 0.1%
90 3760
2.1%
89 98
 
0.1%

weather_id
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean759.8319
Minimum200
Maximum804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-08-21T21:23:08.961704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile500
Q1800
median800
Q3801
95-th percentile803
Maximum804
Range604
Interquartile range (IQR)1

Descriptive statistics

Standard deviation108.73322
Coefficient of variation (CV)0.14310168
Kurtosis7.0304045
Mean759.8319
Median Absolute Deviation (MAD)1
Skewness-2.7478683
Sum1.3555097 × 108
Variance11822.914
MonotonicityNot monotonic
2024-08-21T21:23:09.323249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
800 82685
46.3%
801 32101
 
18.0%
803 17448
 
9.8%
802 15945
 
8.9%
500 10905
 
6.1%
701 3908
 
2.2%
501 3625
 
2.0%
804 2561
 
1.4%
741 2506
 
1.4%
300 1241
 
0.7%
Other values (28) 5471
 
3.1%
ValueCountFrequency (%)
200 92
 
0.1%
201 127
 
0.1%
202 31
 
< 0.1%
210 2
 
< 0.1%
211 789
0.4%
300 1241
0.7%
301 371
 
0.2%
302 14
 
< 0.1%
310 79
 
< 0.1%
311 19
 
< 0.1%
ValueCountFrequency (%)
804 2561
 
1.4%
803 17448
 
9.8%
802 15945
 
8.9%
801 32101
 
18.0%
800 82685
46.3%
771 1
 
< 0.1%
761 345
 
0.2%
741 2506
 
1.4%
731 2
 
< 0.1%
721 435
 
0.2%

weather_main
Categorical

IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
clear
82685 
clouds
68055 
rain
17391 
mist
 
3908
fog
 
2506
Other values (7)
 
3851

Length

Max length12
Median length7
Mean length5.28828
Min length3

Characters and Unicode

Total characters943408
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowclear
2nd rowclear
3rd rowclear
4th rowclear
5th rowclear

Common Values

ValueCountFrequency (%)
clear 82685
46.3%
clouds 68055
38.1%
rain 17391
 
9.7%
mist 3908
 
2.2%
fog 2506
 
1.4%
drizzle 1724
 
1.0%
thunderstorm 1041
 
0.6%
haze 435
 
0.2%
dust 347
 
0.2%
snow 270
 
0.2%
Other values (2) 34
 
< 0.1%

Length

2024-08-21T21:23:09.666971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 82685
46.3%
clouds 68055
38.1%
rain 17391
 
9.7%
mist 3908
 
2.2%
fog 2506
 
1.4%
drizzle 1724
 
1.0%
thunderstorm 1041
 
0.6%
haze 435
 
0.2%
dust 347
 
0.2%
snow 270
 
0.2%
Other values (2) 34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 152466
16.2%
c 150740
16.0%
r 103882
11.0%
a 100512
10.7%
e 85918
9.1%
s 73655
7.8%
o 71905
7.6%
d 71167
7.5%
u 69444
7.4%
i 23023
 
2.4%
Other values (10) 40696
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 943408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 152466
16.2%
c 150740
16.0%
r 103882
11.0%
a 100512
10.7%
e 85918
9.1%
s 73655
7.8%
o 71905
7.6%
d 71167
7.5%
u 69444
7.4%
i 23023
 
2.4%
Other values (10) 40696
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 943408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 152466
16.2%
c 150740
16.0%
r 103882
11.0%
a 100512
10.7%
e 85918
9.1%
s 73655
7.8%
o 71905
7.6%
d 71167
7.5%
u 69444
7.4%
i 23023
 
2.4%
Other values (10) 40696
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 943408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 152466
16.2%
c 150740
16.0%
r 103882
11.0%
a 100512
10.7%
e 85918
9.1%
s 73655
7.8%
o 71905
7.6%
d 71167
7.5%
u 69444
7.4%
i 23023
 
2.4%
Other values (10) 40696
 
4.3%

weather_description
Categorical

IMBALANCE 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
sky is clear
82685 
few clouds
32101 
broken clouds
17448 
scattered clouds
15945 
light rain
10905 
Other values (38)
19312 

Length

Max length28
Median length27
Mean length11.950442
Min length3

Characters and Unicode

Total characters2131911
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowsky is clear
2nd rowsky is clear
3rd rowsky is clear
4th rowsky is clear
5th rowsky is clear

Common Values

ValueCountFrequency (%)
sky is clear 82685
46.3%
few clouds 32101
 
18.0%
broken clouds 17448
 
9.8%
scattered clouds 15945
 
8.9%
light rain 10905
 
6.1%
mist 3908
 
2.2%
moderate rain 3621
 
2.0%
overcast clouds 2561
 
1.4%
fog 2506
 
1.4%
light intensity drizzle 1241
 
0.7%
Other values (33) 5475
 
3.1%

Length

2024-08-21T21:23:09.963824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sky 82685
18.9%
clear 82685
18.9%
is 82685
18.9%
clouds 68055
15.6%
few 32101
 
7.4%
rain 17772
 
4.1%
broken 17448
 
4.0%
scattered 15945
 
3.7%
light 13142
 
3.0%
mist 3908
 
0.9%
Other values (21) 20101
 
4.6%

Most occurring characters

ValueCountFrequency (%)
s 262398
12.3%
258131
12.1%
e 183492
 
8.6%
c 169246
 
7.9%
l 165627
 
7.8%
r 146533
 
6.9%
i 127835
 
6.0%
a 124428
 
5.8%
k 100166
 
4.7%
o 98132
 
4.6%
Other values (16) 495923
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2131911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 262398
12.3%
258131
12.1%
e 183492
 
8.6%
c 169246
 
7.9%
l 165627
 
7.8%
r 146533
 
6.9%
i 127835
 
6.0%
a 124428
 
5.8%
k 100166
 
4.7%
o 98132
 
4.6%
Other values (16) 495923
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2131911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 262398
12.3%
258131
12.1%
e 183492
 
8.6%
c 169246
 
7.9%
l 165627
 
7.8%
r 146533
 
6.9%
i 127835
 
6.0%
a 124428
 
5.8%
k 100166
 
4.7%
o 98132
 
4.6%
Other values (16) 495923
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2131911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 262398
12.3%
258131
12.1%
e 183492
 
8.6%
c 169246
 
7.9%
l 165627
 
7.8%
r 146533
 
6.9%
i 127835
 
6.0%
a 124428
 
5.8%
k 100166
 
4.7%
o 98132
 
4.6%
Other values (16) 495923
23.3%

weather_icon
Categorical

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
01n
38541 
01d
34830 
02d
19199 
02n
12368 
04d
9137 
Other values (19)
64321 

Length

Max length3
Median length3
Mean length2.8906197
Min length2

Characters and Unicode

Total characters515675
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01n
2nd row01n
3rd row01n
4th row01n
5th row01n

Common Values

ValueCountFrequency (%)
01n 38541
21.6%
01d 34830
19.5%
02d 19199
10.8%
02n 12368
 
6.9%
04d 9137
 
5.1%
04n 8403
 
4.7%
03d 7870
 
4.4%
10n 5951
 
3.3%
01 5946
 
3.3%
03n 5833
 
3.3%
Other values (14) 30318
17.0%

Length

2024-08-21T21:23:10.243448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01n 38541
21.6%
01d 34830
19.5%
02d 19199
10.8%
02n 12368
 
6.9%
04d 9137
 
5.1%
04n 8403
 
4.7%
03d 7870
 
4.4%
10n 5951
 
3.3%
01 5946
 
3.3%
03n 5833
 
3.3%
Other values (14) 30318
17.0%

Most occurring characters

ValueCountFrequency (%)
0 177085
34.3%
1 97419
18.9%
d 81590
15.8%
n 77293
15.0%
2 35469
 
6.9%
4 20009
 
3.9%
3 16215
 
3.1%
5 7230
 
1.4%
9 3365
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 515675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177085
34.3%
1 97419
18.9%
d 81590
15.8%
n 77293
15.0%
2 35469
 
6.9%
4 20009
 
3.9%
3 16215
 
3.1%
5 7230
 
1.4%
9 3365
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 515675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177085
34.3%
1 97419
18.9%
d 81590
15.8%
n 77293
15.0%
2 35469
 
6.9%
4 20009
 
3.9%
3 16215
 
3.1%
5 7230
 
1.4%
9 3365
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 515675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177085
34.3%
1 97419
18.9%
d 81590
15.8%
n 77293
15.0%
2 35469
 
6.9%
4 20009
 
3.9%
3 16215
 
3.1%
5 7230
 
1.4%
9 3365
 
0.7%

temp_eq
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
True
109230 
False
69166 
ValueCountFrequency (%)
True 109230
61.2%
False 69166
38.8%
2024-08-21T21:23:10.527702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-08-21T21:22:54.201992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:13.180147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:17.098408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:20.635272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:24.157520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:28.516893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:32.120067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:35.734787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:39.657320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:43.245188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:46.722830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:50.355148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:54.472983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:13.732685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:17.429548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:20.911844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:24.546168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:28.829707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:32.400263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:36.057742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:39.956126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:43.524635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:47.006649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:50.599139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:54.780229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:14.016016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-08-21T21:22:47.287870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:51.304912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-08-21T21:22:14.309980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:17.998128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:21.495028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:25.229222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:29.427692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:32.991550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:36.759661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:40.540340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:44.137227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:47.623708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:51.604908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:55.353657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:14.615032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:18.263698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:21.796986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:25.642533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:29.747064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:33.272739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:37.059129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:40.852979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:44.442687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:47.967252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:51.888058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:55.653595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:14.932328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:18.547905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:22.070893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:26.045716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:30.032318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:33.551245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:37.377883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:41.100883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:44.729957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:48.264628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:52.169640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:55.970178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:15.223771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:18.859523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:22.380144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:26.544436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:30.338233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:33.843861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:37.714253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:41.427361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:45.014226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:48.562502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:52.504570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:56.312997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:15.580142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:19.181026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:22.699038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:26.910544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:30.661798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:34.209716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:38.037152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:41.724448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:45.312176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:48.877060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:52.821229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:56.584626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:15.919413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:19.491554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:22.945566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:27.247798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:31.010529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:34.548657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:38.358681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:42.042183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:45.595452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:49.187227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:53.121056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:56.853252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:16.198581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:19.764320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:23.229853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:27.558613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:31.300623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:34.816492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:38.679314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:42.318918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:45.876642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:49.513153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:53.375550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:57.136633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:16.505734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:20.047467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:23.512242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:27.845178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:31.559167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:35.103497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:39.006432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:42.625542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:46.121528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:49.784649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:53.637569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:57.517978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:16.800254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:20.347439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:23.796380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:28.181304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:31.836741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:35.421304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:39.325105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:42.945700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:46.414823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:50.074442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T21:22:53.920746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-08-21T21:22:57.980102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-21T21:22:58.938079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

city_nametemptemp_mintemp_maxpressurehumiditywind_speedwind_degrain_1hrain_3hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icontemp_eq
dt_iso
2014-12-31 23:00:00+00:00Valencia270.475270.475270.4751001771620.00.00.00800clearsky is clear01nFalse
2015-01-01 00:00:00+00:00Valencia270.475270.475270.4751001771620.00.00.00800clearsky is clear01nFalse
2015-01-01 01:00:00+00:00Valencia269.686269.686269.6861002780230.00.00.00800clearsky is clear01nFalse
2015-01-01 02:00:00+00:00Valencia269.686269.686269.6861002780230.00.00.00800clearsky is clear01nFalse
2015-01-01 03:00:00+00:00Valencia269.686269.686269.6861002780230.00.00.00800clearsky is clear01nFalse
2015-01-01 04:00:00+00:00Valencia270.292270.292270.29210047123210.00.00.00800clearsky is clear01nFalse
2015-01-01 05:00:00+00:00Valencia270.292270.292270.29210047123210.00.00.00800clearsky is clear01nFalse
2015-01-01 06:00:00+00:00Valencia270.292270.292270.29210047123210.00.00.00800clearsky is clear01nFalse
2015-01-01 07:00:00+00:00Valencia274.601274.601274.60110057113070.00.00.00800clearsky is clear01dFalse
2015-01-01 08:00:00+00:00Valencia274.601274.601274.60110057113070.00.00.00800clearsky is clear01dFalse
city_nametemptemp_mintemp_maxpressurehumiditywind_speedwind_degrain_1hrain_3hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icontemp_eq
dt_iso
2018-12-31 13:00:00+00:00Seville289.15289.15289.151029595600.00.00.00800clearsky is clear01dFalse
2018-12-31 14:00:00+00:00Seville290.15290.15290.151028515400.00.00.00800clearsky is clear01dFalse
2018-12-31 15:00:00+00:00Seville291.15291.15291.151028485500.00.00.00800clearsky is clear01dFalse
2018-12-31 16:00:00+00:00Seville291.15291.15291.151028424500.00.00.00800clearsky is clear01dFalse
2018-12-31 17:00:00+00:00Seville291.38290.15292.151028424600.00.00.00800clearsky is clear01dTrue
2018-12-31 18:00:00+00:00Seville287.76287.15288.151028543300.00.00.00800clearsky is clear01nTrue
2018-12-31 19:00:00+00:00Seville285.76285.15286.151029623300.00.00.00800clearsky is clear01nTrue
2018-12-31 20:00:00+00:00Seville285.15285.15285.151028584500.00.00.00800clearsky is clear01nFalse
2018-12-31 21:00:00+00:00Seville284.15284.15284.151029574600.00.00.00800clearsky is clear01nFalse
2018-12-31 22:00:00+00:00Seville283.97282.15285.151029703500.00.00.00800clearsky is clear01nTrue

Duplicate rows

Most frequently occurring

city_nametemptemp_mintemp_maxpressurehumiditywind_speedwind_degrain_1hrain_3hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icontemp_eq# duplicates
948Barcelona296.338296.338296.338102410042430.00.00.00800clearsky is clear01dFalse29
1586Bilbao297.988297.988297.9889866011230.00.00.012801cloudsfew clouds02dFalse29
2383Madrid292.138292.138292.138959940130.00.00.00800clearsky is clear01dFalse29
4949Valencia290.588290.588290.588992920210.00.00.00800clearsky is clear01dFalse29
3636Seville295.688295.688295.68810257522760.00.00.00800clearsky is clear01dFalse27
3637Seville295.688295.688295.68810257522760.00.00.00800clearsky is clear01nFalse17
949Barcelona296.338296.338296.338102410042430.00.00.00800clearsky is clear01nFalse15
1587Bilbao297.988297.988297.9889866011230.00.00.012801cloudsfew clouds02nFalse15
2384Madrid292.138292.138292.138959940130.00.00.00800clearsky is clear01nFalse15
4950Valencia290.588290.588290.588992920210.00.00.00800clearsky is clear01nFalse15